A Detailed Clinical Case of Localized Prostate Tumors Treated with Nanoparticle-Assisted Sub-Ablative Laser Ablation.

AuroLase® Therapy-a nanoparticle-enabled focal therapy-has the potential to safely and effectively treat localized prostate cancer (PCa), preserving baseline functionality. This article presents a detailed case of localized PCa treated with AuroLase, providing insight on expectations from the diagnosis of PCa to one year post-treatment. AuroLase Therapy is a two-day treatment consisting of a systemic infusion of gold nanoshells (~150-nm hydrodynamic diameter) on Day 1, and sub-ablative laser treatment on Day 2. Multiparametric MRI (mpMRI) was used for tumor visualization, treatment planning, and therapy response assessment. The PCa was targeted with a MR/Ultrasound-fusion (MR/US) transperineal approach. Successful treatment was confirmed at 6 and 12 months post-treatment by the absence of disease in MR/US targeted biopsies. On the mpMRI, confined void space was evident, an indication of necrotic tissues encompassing the treated lesion, which was completely resolved at 12 months, forming a band-like scar with no evidence of recurrent tumor. The patient's urinary and sexual functions were unchanged. During the one-year follow-up, changes on the DCE sequence and in the Ktrans and ADC values assist in qualitatively and quantitatively evaluating tissue changes. The results highlight the potential of gold-nanoparticle-enabled sub-ablative laser treatment to target and control localized PCa, maintain quality of life, and preserve baseline functionality.

A contemporary review: mpMRI in prostate cancer screening and diagnosis.

Prostate cancer (PCa) screening has evolved beyond PSA and digital rectal exam to include multiparametric prostate MRI (mpMRI). Incorporating this advanced imaging tool has further limited the well-established problem of overdiagnosis, aiding in the identification of higher grade, clinically significant cancers. For this reason, mpMRI has become an important part of the diagnostic pathway and is recommended across guidelines in biopsy naïve patients or for patients with prior negative biopsy. This contemporary review evaluates the most recent literature on the role of mpMRI in the screening and diagnosis of prostate cancer. Barriers to utilization of mpMRI still exist including variable access, high cost, and requisite expertise, encouraging evaluation of novel techniques such as biparametric MRI. Future screening and diagnostic practice patterns will undoubtedly evolve as our understanding of novel biomarkers and artificial intelligence improves.

The longitudinal changes in multiparametric MRI during neoadjuvant chemotherapy can predict treatment response early in patients with HER2-positive breast cancer.

PURPOSE: To investigate whether longitudinal changes in multiparametric MRI can predict early response to neoadjuvant chemotherapy (NAC) for HER2-positive breast cancer (BC) and to further establish quantitative models based on these features. METHODS: A total of 164 HER2-positive BC patients from three centers were included. MRI was performed at baseline and after two cycles of NAC (early post-NAC). Clinicopathological characteristics were enrolled. MRI features were evaluated at baseline and early post-NAC, as well as longitudinal changes in multiparametric MRI, including changes in the largest diameter (LD) of the tumor (ΔLD), apparent diffusion coefficient (ADC) values (ΔADC), and time-signal intensity curve (TIC) (ΔTIC). The patients were divided into a training set (n = 95), an internal validation set (n = 31), and an independent external validation set (n = 38). Univariate and multivariate logistic regression analyses were used to identify the independent indicators of pCR, which were then used to establish the clinicopathologic model and combined model. The AUC was used to evaluate the predictive power of the different models and calibration curves were used to evaluate the consistency of the prediction of pCR in different models. Additionally, decision curve analysis (DCA) was employed to determine the clinical usefulness of the different models. RESULTS: Two models were enrolled in this study, including the clinicopathologic model and the combined model. The LD at early post-NAC (OR=0.913, 95 % CI=0.953-0.994 p = 0.026), ΔADC (OR=1.005, 95 % CI=1.005-1.008, p = 0.007), and ΔTIC (OR=3.974, 95 % CI=1.276-12.358, p = 0.017) were identified as the best predictors of NAC response. The combined model constructed by the combination of LD at early post-NAC, ΔADC, and ΔTIC showed good predictive performance in the training set (AUC=0.87), internal validation set (AUC=0.78), and external validation set (AUC=0.79), which performed better than the clinicopathologic model in all sets. CONCLUSIONS: The changes in multiparametric MRI can predict early treatment response for HER2-positive BC and may be helpful for individualized treatment planning.

Discriminating Malignant from Benign Testicular Masses Using Multiparametric Magnetic Resonance Imaging-A Prospective Single-Center Study.

Objective: The objective of this study was to prospectively assess the extent to which magnetic resonance imaging (MRI) can differentiate malignant from benign lesions of the testis. Materials and Methods: All included patients underwent multiparametric testicular MRI, including diffusion-weighted imaging (DWI) and subtraction dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI). Subsequently, all patients underwent a histopathological examination via orchiectomy or testicular biopsy/partial resection. The Kolmogorov-Smirnov test, t-test, Mann-Whitney U test, Fisher's exact test, and logistic regression were applied for statistical analysis. Results: We included 48 male patients (median age 37.5 years [range 18-69]) with testicular tumors. The median tumor size on MRI was 2.0 cm for malignant tumors and 1.1 cm for benign tumors (p < 0.05). A statistically significant difference was observed for the type (type 0-III curve, p < 0.05) and pattern of enhancement (homogeneous, heterogeneous, or rim-like, p < 0.01) between malignant and benign tumors. The minimum apparent diffusion coefficient (ADC) value was 0.9 for benign tumors and 0.7 for malignant tumors (each ×103 mm2/s, p < 0.05), while the mean ADC was 0.05. The mean ADC value was significantly lower for malignant tumors; the mean ADC value was 1.1 for benign tumors and 0.9 for malignant tumors (each ×103 mm2/s, p < 0.05). The sensitivity, specificity, positive predictive value, and negative predictive value of multiparametric MRI for differentiating malignant from benign testicular lesions were 94.3%, 76.9%, 91.7%, and 83.3%, respectively. The surgical procedures performed included orchiectomy (n = 33; 71.7%) and partial testicular resection (n = 11; 23.9%). Histopathology (HP) revealed malignancy in 35 patients (72.9%), including 26 with seminomas and 9 with non-seminomatous germ cell tumors (NSGCTs). The HP was benign in 13 (27.1%) patients, including 5 with Leydig cell tumors. Conclusions: Malignant and benign tumors differ in MRI characteristics in terms of the type and pattern of enhancement and the extent of diffusion restriction, indicating that MRI can be an important imaging modality for the accurate diagnosis of testicular lesions.

Biparametric vs. Multiparametric MRI in the Detection of Cancer in Transperineal Targeted-Biopsy-Proven Peripheral Prostate Cancer Lesions Classified as PI-RADS Score 3 or 3+1: The Added Value of ADC Quantification.

BACKGROUND: Biparametric MRI (bpMRI) has an important role in the diagnosis of prostate cancer (PCa), by reducing the cost and duration of the procedure and adverse reactions. We assess the additional benefit of the ADC map in detecting prostate cancer (PCa). Additionally, we examine whether the ADC value correlates with the presence of clinically significant tumors (csPCa). METHODS: 104 peripheral lesions classified as PI-RADS v2.1 score 3 or 3+1 at the mpMRI underwent transperineal MRI/US fusion-guided targeted biopsy. RESULTS: The lesions were classified as PI-RADS 3 or 3+1; at histopathology, 30 were adenocarcinomas, 21 of which were classified as csPCa. The ADC threshold that maximized the Youden index in order to predict the presence of a tumor was 1103 (95% CI (990, 1243)), with a sensitivity of 0.8 and a specificity of 0.59; both values were greater than those found using the contrast medium, which were 0.5 and 0.54, respectively. Similar results were also found with csPCa, where the optimal ADC threshold was 1096 (95% CI (988, 1096)), with a sensitivity of 0.86 and specificity of 0.59, compared to 0.49 and 0.59 observed in the mpMRI. CONCLUSIONS: Our study confirms the possible use of a quantitative parameter (ADC value) in the risk stratification of csPCa, by reducing the number of biopsies and, therefore, the number of unwarranted diagnoses of PCa and the risk of overtreatment.

Evaluation of Machine Learning Classification Models for False-Positive Reduction in Prostate Cancer Detection Using MRI Data.

In this work, several machine learning (ML) algorithms, both classical ML and modern deep learning, were investigated for their ability to improve the performance of a pipeline for the segmentation and classification of prostate lesions using MRI data. The algorithms were used to perform a binary classification of benign and malignant tissue visible in MRI sequences. The model choices include support vector machines (SVMs), random decision forests (RDFs), and multi-layer perceptrons (MLPs), along with radiomic features that are reduced by applying PCA or mRMR feature selection. Modern CNN-based architectures, such as ConvNeXt, ConvNet, and ResNet, were also evaluated in various setups, including transfer learning. To optimize the performance, different approaches were compared and applied to whole images, as well as gland, peripheral zone (PZ), and lesion segmentations. The contribution of this study is an investigation of several ML approaches regarding their performance in prostate cancer (PCa) diagnosis algorithms. This work delivers insights into the applicability of different approaches for this context based on an exhaustive examination. The outcome is a recommendation or preference for which machine learning model or family of models is best suited to optimize an existing pipeline when the model is applied as an upstream filter.

Role of Multiparametric MRI in the Preoperative Evaluation of Endometrial Carcinoma: A Cross-Sectional Study.

Background Endometrial carcinoma (EC) is a major global concern in females throughout the world with increasing incidence in India. Hence, early detection and prompt intervention will reduce morbidity and mortality associated with it. Multiple studies showed a promising role of multiparametric magnetic resonance imaging (mpMRI) in the evaluation and early detection of the disease. In view of the paucity of such studies in the Indian population, we assessed the role of mpMRI in the evaluation of EC by utilizing a 3T MR scanner. Objectives To assess the efficacy of mpMRI in detecting myometrial invasion and locoregional staging in suspected or diagnosed cases of EC. Materials and methods Nineteen cases of EC with mpMRI were included in the study, and 15 of these underwent surgicopathological staging. The preoperative staging was done using the International Federation of Gynecology and Obstetrics (FIGO) 2009 staging system based on mpMRI findings and compared with postoperative FIGO staging. All the data were compiled in a Microsoft Excel (Microsoft® Corp., Redmond, WA) file and analyzed in Statistical Product and Service Solutions (SPSS, version 21.0; IBM SPSS Statistics for Windows, Armonk, NY) using appropriate tools. Results In our study, EC was commonly seen in more than 50-year females with a predominant complaint being postmenopausal bleeding. EC most commonly appeared heterogeneously hyperintense on T2-weighted sequence (T2W) and areas of diffusion restriction on diffusion-weighted imaging (DWI) in all cases. Dynamic contrast-enhanced (DCE) MRI (DCE-MRI) showed mild heterogeneous enhancement in all phases with better delineation of adjacent myometrial infiltration in the equilibrium phase. Diffusion tensor imaging (DTI) parameters had significantly lower values in involved myometrium vis-a-vis uninvolved myometrium. A statistically significant correlation was seen between preoperative mpMRI FIGO staging utilizing T2W, DWI, DCE-MRI, and DTI with surgicopathological FIGO staging. Conclusion mpMRI, particularly T2W, DWI, DCE-MRI, and DTI, yields a significant correlation between MR imaging and histopathological findings in assessing myometrial infiltration and thereby could be helpful in preoperative staging and extent of lymph-nodal dissection.

Multiparametric MRI-Based Deep Learning Radiomics Model for Assessing 5-Year Recurrence Risk in Non-Muscle Invasive Bladder Cancer.

BACKGROUND: Accurately assessing 5-year recurrence rates is crucial for managing non-muscle-invasive bladder carcinoma (NMIBC). However, the European Organization for Research and Treatment of Cancer (EORTC) model exhibits poor performance. PURPOSE: To investigate whether integrating multiparametric MRI (mp-MRI) with clinical factors improves NMIBC 5-year recurrence risk assessment. STUDY TYPE: Retrospective. POPULATION: One hundred ninety-one patients (median age, 65 years; age range, 54-73 years; 27 females) underwent mp-MRI between 2011 and 2017, and received ≥5-year follow-ups. They were divided into a training cohort (N = 115) and validation/testing cohorts (N = 38 in each). Recurrence rates were 23.5% (27/115) in the training cohort and 23.7% (9/38) in both validation and testing cohorts. FIELD STRENGTH/SEQUENCE: 3-T, fast spin echo T2-weighted imaging (T2WI), single-shot echo planar diffusion-weighted imaging (DWI), and volumetric spoiled gradient echo dynamic contrast-enhanced (DCE) sequences. ASSESSMENT: Radiomics and deep learning (DL) features were extracted from the combined region of interest (cROI) including intratumoral and peritumoral areas on mp-MRI. Four models were developed, including clinical, cROI-based radiomics, DL, and clinical-radiomics-DL (CRDL) models. STATISTICAL TESTS: Student's t-tests, DeLong's tests with Bonferroni correction, receiver operating characteristics with the area under the curves (AUCs), Cox proportional hazard analyses, Kaplan-Meier plots, SHapley Additive ExPlanations (SHAP) values, and Akaike information criterion for clinical usefulness. A P-value <0.05 was considered statistically significant. RESULTS: The cROI-based CRDL model showed superior performance (AUC 0.909; 95% CI: 0.792-0.985) compared to other models in the testing cohort for assessing 5-year recurrence in NMIBC. It achieved the highest Harrell's concordance index (0.804; 95% CI: 0.749-0.859) for estimating recurrence-free survival. SHAP analysis further highlighted the substantial role (22%) of the radiomics features in NMIBC recurrence assessment. DATA CONCLUSION: Integrating cROI-based radiomics and DL features from preoperative mp-MRI with clinical factors could improve 5-year recurrence risk assessment in NMIBC. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 3.

Is There an Added Value of Quantitative DCE-MRI by Magnetic Resonance Dispersion Imaging for Prostate Cancer Diagnosis?

In this multicenter, retrospective study, we evaluated the added value of magnetic resonance dispersion imaging (MRDI) to standard multiparametric MRI (mpMRI) for PCa detection. The study included 76 patients, including 51 with clinically significant prostate cancer (csPCa), who underwent radical prostatectomy and had an mpMRI including dynamic contrast-enhanced MRI. Two radiologists performed three separate randomized scorings based on mpMRI, MRDI and mpMRI+MRDI. Radical prostatectomy histopathology was used as the reference standard. Imaging and histopathology were both scored according to the Prostate Imaging-Reporting and Data System V2.0 sector map. Sensitivity and specificity for PCa detection were evaluated for mpMRI, MRDI and mpMRI+MRDI. Inter- and intra-observer variability for both radiologists was evaluated using Cohen's Kappa. On a per-patient level, sensitivity for csPCa for radiologist 1 (R1) for mpMRI, MRDI and mpMRI+MRDI was 0.94, 0.82 and 0.94, respectively. For the second radiologist (R2), these were 0.78, 0.94 and 0.96. R1 detected 4% additional csPCa cases using MRDI compared to mpMRI, and R2 detected 20% extra csPCa cases using MRDI. Inter-observer agreement was significant only for MRDI (Cohen's Kappa = 0.4250, p = 0.004). The results of this study show the potential of MRDI to improve inter-observer variability and the detection of csPCa.

Radiomics using multiparametric magnetic resonance imaging to predict postoperative visual outcomes of patients with pituitary adenoma.

BACKGROUND: Preoperative prediction of visual outcomes following pituitary adenoma surgery is challenging yet crucial for clinical decision-making. We aimed to develop models using radiomics from multiparametric MRI to predict postoperative visual outcomes. METHODS: A cohort of 152 patients with pituitary adenoma was retrospectively enrolled and divided into recovery and non-recovery groups based on visual examinations performed six months after surgery. Radiomic features of the optic chiasm were extracted from preoperative T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), and contrast-enhanced T1-weighted imaging (T1CE). Predictive models were constructed using the least absolute shrinkage and selection operator wrapped with a support vector machine through five-fold cross-validation in the development cohort and evaluated in an independent test cohort. Model performance was evaluated using the area under the curve (AUC), accuracy, sensitivity, and specificity. RESULTS: Four models were established based on radiomic features selected from individual or combined sequences. The AUC values of the models based on T1WI, T2WI and T1CE were 0.784, 0.724, 0.822 in the development cohort, and 0.767, 0.763, 0.794 in the independent test cohort. The multiparametric model demonstrated superior performance among the four models, with AUC of 0.851, accuracy of 0.832. sensitivity of 0.700, specificity of 0.910 in the development cohort, and AUC of 0.847, accuracy of 0.800, sensitivity of 0.882 and specificity of 0.750 in the independent test cohort. CONCLUSION: The multiparametric model utilizing radiomics of optic chiasm outperformed single-sequence models in predicting postoperative visual recovery in patients with pituitary adenoma, serving as a novel approach for enhancing personalized treatment strategies.

Comparison of staging MRI to re-resection for localised bladder cancer: Narrative review.

Introduction: Bladder cancer (BCa) is characterised by high prevalence, multifocality, and frequent recurrence, imposing significant clinical and economic burdens. Accurate staging, particularly distinguishing non-muscle-invasive bladder cancer (NMIBC) from muscle-invasive bladder cancer (MIBC) disease, is crucial for guiding treatment decisions. This narrative review explores the potential implications of incorporating multiparametric magnetic resonance imaging (mpMRI) and the Vesical Imaging Reporting Data System (VI-RADS) into BCa staging, focusing on repeat transurethral resection of bladder tumour (re-TURBT). Methods: A comprehensive search of PubMed, EMBASE, and MEDLINE databases identified studies published from 2018 to 2023 discussing mpMRI or VI-RADS in the context of re-TURBT for BCa staging. Studies meeting inclusion criteria underwent qualitative analysis. Results: Six recent studies met inclusion criteria. VI-RADS scoring, accurately predicted muscle invasion, aiding in NMIBC/MIBC differentiation. VI-RADS scores of ≥3 indicated MIBC with high sensitivity and specificity. VI-RADS potentially identified patients benefiting from re-TURBT and those for whom it could be safely omitted. Discussion: mpMRI and VI-RADS offer promising prospects for BCa staging, potentially correlating more closely with re-TURBT and radical cystectomy histopathology than initial TURBT. However, validation and careful evaluation of clinical integration are needed. Future research should refine patient selection and optimise mpMRI's role in BCa management. Conclusion: VI-RADS scoring could revolutionise BCa staging, especially regarding re-TURBT. There is potential that VI-RADS correlates more with the histopathology of re-TURBT and radical cystectomy than initial TURBT. While promising, ongoing research is essential to validate utility, refine selection criteria, and address economic considerations. Integration of VI-RADS into BCa staging holds potential benefits for patients and health care systems.

Prostate MRI for the detection of clinically significant prostate cancer: Update and future directions.

PURPOSE OF REVIEW: In recent decades, there has been an increasing role for magnetic resonance imaging (MRI) in the detection of clinically significant prostate cancer (csPC). The purpose of this review is to provide an update and outline future directions for the role of MRI in the detection of csPC. RECENT FINDINGS: In diagnosing clinically significant prostate cancer pre-biopsy, advances include our understanding of MRI-targeted biopsy, the role of biparametric MRI (non-contrast) and changing indications, for example the role of MRI in screening for prostate cancer. Furthermore, the role of MRI in identifying csPC is maturing, with emphasis on standardization of MRI reporting in active surveillance (PRECISE), clinical staging (EPE grading, MET-RADS-P) and recurrent disease (PI-RR, PI-FAB). Future directions of prostate MRI in detecting csPC include quality improvement, artificial intelligence and radiomics, positron emission tomography (PET)/MRI and MRI-directed therapy. SUMMARY: The utility of MRI in detecting csPC has been demonstrated in many clinical scenarios, initially from simply diagnosing csPC pre-biopsy, now to screening, active surveillance, clinical staging, and detection of recurrent disease. Continued efforts should be undertaken not only to emphasize the reporting of prostate MRI quality, but to standardize reporting according to the appropriate clinical setting.

Factors associated with pathological up-staging in MRI cT3a prostate cancer - a retrospective study from a high-volume centre.

INTRODUCTION: Multiparametric MRI (mpMRI) parameters of pT3a prostate cancer have not been examined in large cohort studies. Therefore, we aimed to identify factors associated with up-staging of mpMRI cT3a in post-operative histopathological confirmation. METHODS: Retrospective analysis of a prospectively maintained database of a single UK cancer centre. Only cT3a cases who underwent robotic-assisted radical prostatectomy (RARP) were included (N = 383). MRI and specimen histopathology was reviewed independently by expert uro-radiologists and uro-histopathologists, respectively. Factors included age, BMI, prostate-specific antigen (PSA) level, biopsy international society of urological pathology (ISUP) grade, Prostate Imaging Reporting & Data System (PI-RADS®) score, tumour size, tumour coverage of gland (%), gland weight and surgical margins were analysed as predictors of pT3a prostate cancer. RESULTS: N = 383. Mean age 66 years (58-71), mean BMI 27.1 kg/m2 (25.0-30.0). 314 (82.0%) cases down- unchanged or down-staged, and 69 (18.0%) cases upstaged. PSA level (P = 0.002), PI-RADS score (P < 0.001) and ISUP grade (P < 0.001) are positively associated with upstage categories. ISUP grade ≥3 (OR 5.45, CI 1.88, 9.29, P < 0.002), PI-RADS score ≥4 (OR 3.92, CI 1.88-9.29, P < 0.001) and tumour coverage (OR 1.06, CI 1.05-1.08, P < 0.001) significantly positively associated with upstaging disease, with concurrent decreased probability of downstaging (OR 0.55, 0.14, 0.44, respectively, P < 0.05). Tumour coverage was positively correlated with increasing positive surgical margins (P < 0.05). Capsular contact > 15 mm was very unlikely to be upstaged (OR 0.36, CI 0.21-0.62, P < 0.001), aligning with published results past the widely accepted significant level for extracapsular disease on MRI. CONCLUSION: The study has identified PSA level, ISUP, PI-RADS score, tumour volume and percentage coverage are key predictive factors in cT3a upstaging. This study uniquely shows tumour coverage percentage as a predictor of cT3a upstaging on mpMRI. ISUP is the strongest predictor, followed by PI-RADS score and tumour coverage of gland. Multi-institutional studies are needed to confirm our findings.

Comparison of data fusion strategies for automated prostate lesion detection using mpMRI correlated with whole mount histology.

BACKGROUND: In this work, we compare input level, feature level and decision level data fusion techniques for automatic detection of clinically significant prostate lesions (csPCa). METHODS: Multiple deep learning CNN architectures were developed using the Unet as the baseline. The CNNs use both multiparametric MRI images (T2W, ADC, and High b-value) and quantitative clinical data (prostate specific antigen (PSA), PSA density (PSAD), prostate gland volume & gross tumor volume (GTV)), and only mp-MRI images (n = 118), as input. In addition, co-registered ground truth data from whole mount histopathology images (n = 22) were used as a test set for evaluation. RESULTS: The CNNs achieved for early/intermediate / late level fusion a precision of 0.41/0.51/0.61, recall value of 0.18/0.22/0.25, an average precision of 0.13 / 0.19 / 0.27, and F scores of 0.55/0.67/ 0.76. Dice Sorensen Coefficient (DSC) was used to evaluate the influence of combining mpMRI with parametric clinical data for the detection of csPCa. We compared the DSC between the predictions of CNN's trained with mpMRI and parametric clinical and the CNN's trained with only mpMRI images as input with the ground truth. We obtained a DSC of data 0.30/0.34/0.36 and 0.26/0.33/0.34 respectively. Additionally, we evaluated the influence of each mpMRI input channel for the task of csPCa detection and obtained a DSC of 0.14 / 0.25 / 0.28. CONCLUSION: The results show that the decision level fusion network performs better for the task of prostate lesion detection. Combining mpMRI data with quantitative clinical data does not show significant differences between these networks (p = 0.26/0.62/0.85). The results show that CNNs trained with all mpMRI data outperform CNNs with less input channels which is consistent with current clinical protocols where the same input is used for PI-RADS lesion scoring. TRIAL REGISTRATION: The trial was registered retrospectively at the German Register for Clinical Studies (DRKS) under proposal number Nr. 476/14 & 476/19.

Molecular and diffusion features for identification of clinically significant prostate cancer in PI-RADS 3 lesions.

BACKGROUND: The recommendation to perform biopsy of PIRADS 3 lesions has not been adopted with strength as compared to higher scored lesions on multiparametric MRI. This represents a challenging scenario and an unmet need for clinicians to apply a risk adapted approach in these cases. In the present study, we examined clinical and radiologic characteristics in men with PI-RADS 3 index lesions that can predict csPCa on mpMRI-target biopsy. METHODS: Revision of a prospective database with patients who underwent targeted and systematic biopsies from 2015 to 2023 for PI-RADS 3 lesions identified on mpMRI. Baseline variables were collected, such as PSA density (PSAd), 4Kscore, prostate size, and the apparent diffusion coefficient (ADC) value of the lesion on mpMRI. Logistic regression, receiver operating characteristic (ROC) and decision curve analyses (DCA) assessing the association between clinic-radiologic factors and csPCa were performed. RESULTS: Overall, 230 patients were included in the study and the median age was 65 years. The median prostate size and PSA were 50 g and 6.26 ng/mL, respectively. 17.4% of patients had csPCa, while 27.5% had Gleason group 1. In univariable logistic analyses, we found that age, BMI, prostate size, PSAd, ADC, and 4Kscore were significant csPCa predictors (P < 0.05). PSAd showed the best prediction performance in terms of AUC (= 0.679). On multivariable analysis, PSAd and 4Kscore were associated with csPCa. The net benefit of PSAd combined with clinical features was superior to those of other parameters. Within patients with PSAd < 0.15, 4Kscore was a statistically significant predictor of csPCa (OR = 3.25, P = 0.032). CONCLUSION: PSAd and 4Kscore are better predictors of csPCa in patients with PIRADS 3 lesions compared to ADC. The predictive role of 4Kscore is higher in patients with low PSAd. These results can assist practitioners in the risk stratification of patients with equivocal lesions to determine the need of biopsy.

Optimizing Prostate Imaging Practices in Saudi Arabian Hospitals: A Comprehensive Analysis of PI-RADS Compliance in Multiparametric MRI.

BACKGROUND: Prostate cancer, a significant contributor to male cancer mortality globally, demands improved diagnostic strategies. In Saudi Arabia, where the incidence is expected to double, this study assessed the compliance of multiparametric MRI (mpMRI) practices with Prostate Imaging-Reporting and Data System version 2 (PI-RADS v2) guidelines across diverse healthcare institutions. METHODS: A survey was distributed to the radiology departments of all tertiary referral hospitals in Saudi Arabia (n=60) to assess their compliance with the technical specifications outlined in PI-RADS v2. Statistical analysis included chi-square, Fisher exact, ANOVA, and Student t-tests to examine the collected data. RESULTS: The study revealed an overall commendable compliance rate of 95.23%. However, significant variations were observed in technical parameters, particularly between 1.5 Tesla and 3 Tesla scanners and tertiary versus non-tertiary hospitals. Notable adherence in certain sequences contrasted with discrepancies in T2-weighted and diffusion-weighted imaging parameters. CONCLUSION: These findings underscore the need for nuanced approaches to optimize prostate imaging protocols, considering field strength and institutional differences. The study contributes to the ongoing refinement of standardized mpMRI practices, aiming to enhance diagnostic accuracy and improve clinical outcomes in prostate cancer.

Preoperative Prediction of Muscle Invasiveness in Bladder Cancer: The Role of 3D Volumetric Radiomics Using Diffusion-Weighted MRI, the VI-RADS Score, or a Combination of Both.

BACKGROUND: Bladder cancer treatment decisions hinge on detecting muscle invasion. The 2018 "Vesical Imaging Reporting and Data System" (VI-RADS) standardizes multiparametric MRI (mp-MRI) use. Radiomics, an analysis framework, provides more insightful information than conventional methods. PURPOSE: To determine how well MIBC (Muscle Invasive Bladder Cancer) and NMIBC (Non-Muscle Invasive Bladder Cancer) can be distinguished using mp-MRI radiomics features. METHODS: We conducted a study with 73 bladder cancer patients diagnosed pathologically, who underwent preoperative mp-MRI from January 2020 to July 2022. Utilizing 3D Slicer (version 4.8.1) and Pyradiomics, we manually extracted radiomic features from apparent diffusion coefficient (ADC) maps created from diffusion-weighted imaging. The LASSO approach identified optimal features, and we addressed sample imbalance using SMOTE. We developed a classification model using textural features alone or combined with VI-RADS, employing a random forest classifier with 10-fold cross-validation. Diagnostic performance was assessed using the area under the ROC curve analysis. RESULTS: Among 73 patients (63 men, 10 women; median age: 63 years), 41 had muscle-invasive and 32 had superficial bladder cancer. Muscle invasion was observed in 25 of 41 patients with VI-RADS 4 and 5 scores and 12 of 32 patients with VI-RADS 1, 2, and 3 scores (accuracy: 77.5%, sensitivity: 67.7%, specificity: 88.8%). The combined VI-RADS score and radiomics model (AUC = 0.92 ± 0.12) outperformed the single radiomics model using ADC MRI (AUC = 0.83 ± 0.22 with 10-fold cross-validation) in this dataset. CONCLUSION: Before undergoing surgery, bladder cancer invasion in muscle might potentially be predicted using a radiomics signature based on mp-MRI.

Comparison of Ga-68 PSMA PET/CT and Multiparametric MRI for Initial Detection and Staging of Prostate Cancer.

Background Multiparametric magnetic resonance imaging (mpMRI) is widely used for the evaluation of prostate cancer and is known to have better accuracy. Gallium-68 prostate-specific membrane antigen (Ga-68 PSMA) is a radiotracer that shows high localization in prostate cancer cells. Purpose The purpose of this study was to assess the sensitivity and utility of Ga-68 PSMA positron emission tomography/computed tomography (PET/CT) in comparison with mpMRI as a noninvasive imaging technique for the initial diagnosis and locoregional staging of prostate cancer using transrectal ultrasound (TRUS)-guided biopsy as gold standard. Materials and Methods This prospective observational study conducted from August 2017 to April 2020 evaluated 60 men ( n = 60) with biopsy-proven prostate carcinoma. They underwent mpMRI and Ga-68 PSMA PET/CT scans within 14 days with TRUS biopsy being gold standard. T staging of disease, N staging of lymph nodes within the pelvis, and M staging of lesions in pelvic bones (within the imaging field of mpMRI) were compared using PSPP version 1.0.1 statistical software. Results All 60 men with a mean age of 69.9 ± 9.35 years showed Ga-68 PSMA avid disease, whereas 55 were detected by mpMRI. The sensitivity in detection of prostate lesions (with 95% confidence interval) was 99.08% for Ga-68 PSMA PET/CT and 84.40% for mpMRI. Ga-68 PSMA PET/CT detected greater number of patients with regional lymph nodal involvement (19/60) as compared with mpMRI (12/60). Ga-68 PSMA PET/CT showed PSMA avid pelvic skeletal lesions in nine patients, whereas mpMRI detected pelvic lesions in six patients. In addition, four other patients showed extrapelvic skeletal lesions on Ga-68 PSMA PET/CT. Conclusion Ga-68 PSMA PET/CT has superior sensitivity in detection of primary prostate tumor, as compared with mpMRI. Both modalities correlate well in detection of seminal vesicle involvement. Ga-68 PSMA PET/CT outperformed mpMRI in detection of lymph nodal and skeletal metastases. Hence, Ga-68 PSMA PET/CT should be considered as first-line diagnostic modality for carcinoma prostate. Summary Statement : Ga-68 PSMA PET/CT shows superior diagnostic performance than mpMRI in the evaluation of prostate cancer.

Impact of prostate MRI image quality on diagnostic performance for clinically significant prostate cancer (csPCa).

OBJECTIVES: With the widespread clinical application of prostate magnetic resonance imaging (MRI), there has been an increasing demand for lesion detection and accurate diagnosis in prostate MR, which relies heavily on satisfactory image quality. Focusing on the primary sequences involved in Prostate Imaging Reporting and Data System (PI-RADS), this study have evaluated common quality issues in clinical practice (such as signal-to-noise ratio (SNR), artifacts, boundaries, and enhancement). The aim of the study was to determine the impact of image quality on clinically significant prostate cancer (csPCa) detection, positive predictive value (PPV) and radiologist's diagnosis in different sequences and prostate zones. METHODS: This retrospective study included 306 patients who underwent prostate MRI with definitive pathological reports from February 2021 to December 2022. All histopathological specimens were evaluated according to the recommendations of the International Society of Urological Pathology (ISUP). An ISUP Grade Group ≥ 2 was considered as csPCa. Three radiologists from different centers respectively performed a binary classification assessment of image quality in the following ten aspects: (1) T2WI in the axial plane: SNR, prostate boundary conditions, the presence of artifacts; (2) T2WI in the sagittal or coronal plane: prostate boundary conditions; (3) DWI: SNR, delineation between the peripheral and transition zone, the presence of artifacts, the matching of DWI and T2WI images; (4) DCE: the evaluation of obturator artery enhancement, the evaluation of dynamic contrast enhancement. Fleiss' Kappa was used to determine the inter-reader agreement. Wilson's 95% confidence interval (95% CI) was used to calculate PPV. Chi-square test was used to calculate statistical significance. A p-value < 0.05 was considered statistically significant. RESULTS: High-quality images had a higher csPCa detection rate (56.5% to 64.3%) in axial T2WI, DWI, and DCE, with significant statistical differences in SNR in axial T2WI (p 0.002), the presence of artifacts in axial T2WI (p 0.044), the presence of artifacts in DWI (p < 0.001), and the matching of DWI and T2WI images (p < 0.001). High-quality images had a higher PPV (72.5% to 78.8%) and showed significant statistical significance in axial T2WI, DWI, and DCE. Additionally, we found that PI-RADS 3 (24.0% to 52.9%) contained more low-quality images compared to PI-RADS 4-5 (20.6% to 39.3%), with significant statistical differences in the prostate boundary conditions in axial T2WI (p 0.048) and the presence of artifacts in DWI (p 0.001). Regarding the relationship between csPCa detection and image quality in different prostate zones, this study found that significant statistical differences were only observed between high- (63.5% to 75.7%) and low-quality (30.0% to 50.0%) images in the peripheral zone (PZ). CONCLUSION: Prostate MRI quality may have an impact on the diagnostic performance. The poorer image quality is associated with lower csPCa detection rates and PPV, which can lead to an increase in radiologist's ambiguous diagnosis (PI-RADS 3), especially for the lesions located at PZ.

Metabolic Insight into Glioma Heterogeneity: Mapping Whole Exome Sequencing to In Vivo Imaging with Stereotactic Localization and Deep Learning.

Intratumoral heterogeneity (ITH) complicates the diagnosis and treatment of glioma, partly due to the diverse metabolic profiles driven by underlying genomic alterations. While multiparametric imaging enhances the characterization of ITH by capturing both spatial and functional variations, it falls short in directly assessing the metabolic activities that underpin these phenotypic differences. This gap stems from the challenge of integrating easily accessible, colocated pathology and detailed genomic data with metabolic insights. This study presents a multifaceted approach combining stereotactic biopsy with standard clinical open-craniotomy for sample collection, voxel-wise analysis of MR images, regression-based GAM, and whole-exome sequencing. This work aims to demonstrate the potential of machine learning algorithms to predict variations in cellular and molecular tumor characteristics. This retrospective study enrolled ten treatment-naïve patients with radiologically confirmed glioma. Each patient underwent a multiparametric MR scan (T1W, T1W-CE, T2W, T2W-FLAIR, DWI) prior to surgery. During standard craniotomy, at least 1 stereotactic biopsy was collected from each patient, with screenshots of the sample locations saved for spatial registration to pre-surgical MR data. Whole-exome sequencing was performed on flash-frozen tumor samples, prioritizing the signatures of five glioma-related genes: IDH1, TP53, EGFR, PIK3CA, and NF1. Regression was implemented with a GAM using a univariate shape function for each predictor. Standard receiver operating characteristic (ROC) analyses were used to evaluate detection, with AUC (area under curve) calculated for each gene target and MR contrast combination. Mean AUC for five gene targets and 31 MR contrast combinations was 0.75 ± 0.11; individual AUCs were as high as 0.96 for both IDH1 and TP53 with T2W-FLAIR and ADC, and 0.99 for EGFR with T2W and ADC. These results suggest the possibility of predicting exome-wide mutation events from noninvasive, in vivo imaging by combining stereotactic localization of glioma samples and a semi-parametric deep learning method. The genomic alterations identified, particularly in IDH1, TP53, EGFR, PIK3CA, and NF1, are known to play pivotal roles in metabolic pathways driving glioma heterogeneity. Our methodology, therefore, indirectly sheds light on the metabolic landscape of glioma through the lens of these critical genomic markers, suggesting a complex interplay between tumor genomics and metabolism. This approach holds potential for refining targeted therapy by better addressing the genomic heterogeneity of glioma tumors.